AI-Powered Cyber Threat Intelligence: Dark Web Analysis & Proactive Defense

Day 1: Introduction to Cyber Threat Intelligence (CTI) and Threat Actors

• Overview of CTI, its lifecycle, and types of intelligence

• Understanding threat actors and attack vectors (phishing, malware, APTs)

• OSINT tools: WHOIS, Shodan, Maltego

Day 2: Collection and Processing of Threat Data

• Sources: Open-source, closed-source, and internal data

• Data collection techniques: Passive vs active, legal and ethical considerations

• Using tools like MISP and ThreatConnect for IOC analysis

Day 3: Advanced Threat Analysis and Attribution

• Techniques for anomaly detection, pattern recognition, and correlation

• Malware analysis for CTI: Static vs dynamic

• Threat attribution and case studies (APT29, Lazarus Group)

Day 4: AI-Powered Threat Detection and Monitoring

• Introduction to AI/ML applications in CTI

• Building and applying threat detection models

• Practical lab: Using AI for real-time threat monitoring

Day 5: Operational Integration and Threat Intelligence Sharing

• Integrating CTI into SIEM and SOAR platforms

• Automating threat detection and response

• Sharing intelligence: ISACs, STIX/TAXII standards

Day 6: Capstone Project and Final Exercise

• Simulated cyber incident scenario

• Collecting and analyzing threat intelligence

• Creating actionable intelligence reports and recommendations